You can build AI-text detectors that adaptively choose what to inspect and when to stop, while mathematically guaranteeing false-alert control without needing to split your error budget across all possible inspection paths.
This paper develops statistical methods to control false alerts when screening documents for AI-generated text. The authors propose two conformal inference approaches that allow adaptive inspection—selecting which parts of documents to check and when to stop—while guaranteeing that false-alert rates stay below a target threshold, even when checking multiple detection strategies.